简介:
mapreduce是一种编程模型,用于大规模数据集(大于1TB)的并行运算。
它通过把对数据集的大规模操作分发给网络上的每个节点实现可靠性,极大地方便了编程人员在不会分布式并行编程的情况下,将自己的程序运行在分布式系统上。
MapReduce自带的分区器是HashPartitioner
原理:
先对map输出的key求hash值,再模上reduce task个数,根据结果,决定此输出kv对,被匹配的reduce任务取走。
自定义分分区需要继承Partitioner
,复写getpariton()
方法
自定义分区类:
注意:map的输出是<K,V>键值对
其中int partitionIndex = dict.get(text.toString())
,partitionIndex
是获取K的值
附:被计算的的文本
Dear Dear Bear Bear River Car Dear Dear Bear Rive
Dear Dear Bear Bear River Car Dear Dear Bear Rive
需要在main函数中设置,指定自定义分区类
自定义分区类:
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
import java.util.HashMap;
public class CustomPartitioner extends Partitioner<Text, IntWritable> {
public static HashMap<String, Integer> dict = new HashMap<String, Integer>();
//Text代表着map阶段输出的key,IntWritable代表着输出的值
static{
dict.put("Dear", 0);
dict.put("Bear", 1);
dict.put("River", 2);
dict.put("Car", 3);
}
public int getPartition(Text text, IntWritable intWritable, int i) {
//
int partitionIndex = dict.get(text.toString());
return partitionIndex;
}
}
注意:map的输出结果是键值对<K,V>,int partitionIndex = dict.get(text.toString());
中的partitionIndex
是map输出键值对中的键的值,也就是K的值。
Maper类:
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable> {
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] words = value.toString().split("\t");
for (String word : words) {
// 每个单词出现1次,作为中间结果输出
context.write(new Text(word), new IntWritable(1));
}
}
}
Reducer类:
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable> {
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] words = value.toString().split("\t");
for (String word : words) {
// 每个单词出现1次,作为中间结果输出
context.write(new Text(word), new IntWritable(1));
}
}
}
main函数:
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
public class WordCountMain {
public static void main(String[] args) throws IOException,
ClassNotFoundException, InterruptedException {
if (args.length != 2 || args == null) {
System.out.println("please input Path!");
System.exit(0);
}
Configuration configuration = new Configuration();
configuration.set("mapreduce.job.jar","/home/bruce/project/kkbhdp01/target/com.kaikeba.hadoop-1.0-SNAPSHOT.jar");
Job job = Job.getInstance(configuration, WordCountMain.class.getSimpleName());
// 打jar包
job.setJarByClass(WordCountMain.class);
// 通过job设置输入/输出格式
//job.setInputFormatClass(TextInputFormat.class);
//job.setOutputFormatClass(TextOutputFormat.class);
// 设置输入/输出路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 设置处理Map/Reduce阶段的类
job.setMapperClass(WordCountMap.class);
//map combine
//job.setCombinerClass(WordCountReduce.class);
job.setReducerClass(WordCountReduce.class);
//如果map、reduce的输出的kv对类型一致,直接设置reduce的输出的kv对就行;如果不一样,需要分别设置map, reduce的输出的kv类型
//job.setMapOutputKeyClass(.class)
// 设置最终输出key/value的类型m
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setPartitionerClass(CustomPartitioner.class);
job.setNumReduceTasks(4);
// 提交作业
job.waitForCompletion(true);
}
}
main函数参数设置:
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